Tag Archives: tutorial

I’m going to give a Python Pandas guest lecture in the Python Science class next week, and I thought I’d take a look at the Pandas intros that are out there. There are a lot now! Here are some that I flipped through:

This body of research is quite far from the vocabulary I am familiar with, so I’m not sure how serious a problem this could be for me. It did get me interested in sticking my statistical model to a systems model with stochastic dynamics, though, something which took only a few additional lines… thanks PyMC!

This ends up taking a total of 6 lines more than the deterministic version, and all the substantial changes are from lines 24-34. So, question one is for Anand, do I have to worry about unobserved sample paths here? If I’ve understood Roberts and Stramer’s introduction, I should be ok. Question two returns to a blog topic from one year ago, that I’ve continued to try to educate myself about: how do I decide if and when this more complicated model should be used?

A recent question on the PyMC mailing list inspired me. How can you estimate transition parameters in a compartmental model? I did a lit search for just this when I started up my generic disease modeling project two years ago. Much information, I did not find. I turned up one paper which said basically that using a Bayesian approach was a great idea and someone should try it (and I can’t even find that now!).

Part of the problem was language. I’ve since learned that micro-simulators call it “calibration” when you estimate parameter values, and there is a whole community of researchers working on “black-box modeling plug-and-play inference” that do something similar as well. These magic phrases are incantations to the search engines that help find some relevant prior work.

But I started blazing my own path before I learned any of the right words; with PyMC, it is relatively simple. Consider the classic SIR model from mathematical epidemology. It’s a great place to start, and it’s what Jason Andrews started with on the PyMC list. I’ll show you how to formulate it for Bayesian parameter estimation in PyMC, and how to make sure your MCMC has run for long enough. Continue reading →

I never took a statistics class, so I only know the kind of statistics you learn on the street. But now that I’m in global health research, I’ve been doing a lot of on-the-job learning. This post is about something I’ve been reading about recently, how to decide if a simple statistical model is sufficient or if the data demands a more complicated one. To keep the matter concrete (and controversial) I’ll focus on a claim from a recent paper in Nature that my colleague, Haidong Wang, choose for our IHME journal club last week: Advances in development reverse fertility declines. The title of this short letter boldly claims a causal link between total fertility rate (an instantaneous measure of how many babies a population is making) and the human development index (a composite measure of how “developed” a country is, on a scale of 0 to 1). Exhibit A in their case is the following figure:

An astute observer of this chart might ask, “what’s up with the scales on those axes?” But this post is not about the visual display of quantitative information. It is about deciding if the data has a piecewise linear relationship that Myrskyla et al claim, and doing it in a Bayesian framework with Python and PyMC. But let’s start with a figure where the axes have a familiar linear scale! Continue reading →

I’ve got an urge to write another introductory tutorial for the Python MCMC package PyMC. This time, I say enough to the comfortable realm of Markov Chains for their own sake. In this tutorial, I’ll test the waters of Bayesian probability.

Tom Bayes

Now, what better problem to stick my toe in than the one that inspired Reverend Thomas in the first place? Let’s talk about sex ratio. This is also convenient, because I can crib from Bayesian Data Analysis, that book Rif recommended me a month ago.

Bayes started this enterprise off with a question that has inspired many an evolutionary biologist: are girl children as likely as boy children? Or are they more likely or less likely? Laplace wondered this also, and in his time and place (from 1745 to 1770 in Paris) there were birth records of 241,945 girls and 251,527 boys. In the USA in 2005, the vital registration system recorded 2,118,982 male and 2,019,367 female live births [1]. I’ll set up a Bayesian model of this, and ask PyMC if the sex ratio could really be 1.0.

This post is a little tutorial on how to use PyMC to sample points uniformly at random from a convex body. This computational challenge says: if you have a magic box which will tell you yes/no when you ask, “Is this point (in n-dimensions) in the convex set S”, can you come up with a random point which is nearly uniformly distributed over S?

MCMC has been the main approach to solving this problem, and it has been a great success for the polynomial-time dogma, starting with the work of Dyer, Frieze, and Kannan which established the running-time upper bound of . The idea is this: you start with some point in S and try to move to a new, nearby point randomly. “Randomly how?”, you wonder. That is the art. Continue reading →